Abstract:We explore the merits of neural network boosted, principal-component-projection-based, unsupervised data classification in singlemolecule break junction measurements, demonstrating that this method identifies highly relevant trace classes according to the welldefined and well-visualized internal correlations of the dataset. To this end, we investigate single-molecule structures exhibiting double molecular configurations, exploring the role of the leading principal components in the identification of alternativ… Show more
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